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Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Hong, Danfeng and Yokoya, Naoto and Xu, Jian and Zhu, Xiao Xiang (2018) Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification. European Conference on Computer Vision (ECCV) 2018, 08.-14.9.2018, Munich, Germany. ISBN 978-3-030-01237-3

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Official URL: https://eccv2018.org/

Abstract

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called joint and progressive learning strategy (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/120797/
Document Type:Conference or Workshop Item (Poster)
Title:Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Xu, Jianjian.xu (at) dlr.deUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:2018
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-16
Series Name:Lecture Notes in Computer Science
ISBN:978-3-030-01237-3
Status:Published
Keywords:Alternating direction method of multipliers, high-dimensional data, manifold regularization, multi-label classification, joint learning, progressive learning
Event Title:European Conference on Computer Vision (ECCV) 2018
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:08.-14.9.2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hong, Danfeng
Deposited On:04 Jul 2018 13:29
Last Modified:31 Jul 2019 20:18

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